Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL), what do they all have in common? They are all concepts that most people aren’t familiar with, especially to those who aren’t so big into tech. So to get you started, I’ve listed some components of AI to help you better understand what it actually is and how it can shape your view of technology in the future.
When Did AI Begin?
Artificial Intelligence (AI) has been around for quite some time. Even in Greek mythology, Hephaestus, the god of metal, created intelligent robots to carry out duties normally attributed to humans. In the 19th and 20th centuries, sentient machines gained notoriety through fictions like the Frankenstein, Rossum’s Universal Robots, and A Space Odyssey. Over time, philosophers, scientists, engineers, and mathematicians from all around the world contributed to the development of AI. And in 1956, for the first time in history, the field of AI research officially became an academic discipline at the Dartmouth Conference.
So What Exactly Is Artificial Intelligence?
AI in simple terms is a branch of computer science that designs intelligent machines to think and learn like a human being. AI studies how the human brain works and imitates their behaviors. It can understand different languages, learn new concepts, recognize objects and sounds, and solve problems. Basically, AI parallels human thoughts and actions, which means it has the power to carry out functions generally done by humans.
The scope of AI is vast, filled with big words and complicated ideas. But two most important concepts I want to cover are Machine Learning and Deep Learning.
Machine Learning
At the core of AI, there is Machine Learning. Think of ML as an approach to AI, solving real-world issues with systems that mirror the human brain. In other words, ML allows machines to think for themselves. Machine Learning consists of complicated math and coding, and it serves a very mechanical function. By this, I mean that the machine will perform a specific task once it receives a particular data. And over time, it will become better at doing that task. ML models aren’t perfect, and they will sometimes produce inaccurate predictions. That is why they need an engineer who can step in once or twice to make some changes.
One type of Machine Learning is called Reinforcement Learning (RL). RL focuses on completing tasks through a system of rewards and punishments. Unlike other algorithms in ML, this approach specializes in solving problems on its own. An agent, or an RL algorithm, receives rewards by completing tasks correctly and punishments by performing them incorrectly. Many pet owners use this method to teach their animals how to do a certain task. Our very own KOSHO app, which is coming out real soon, utilizes RL to help incentivize individual investors with gift vouchers and highly detailed stock portfolios.
Deep Learning
Deep Learning is essentially the same technology as ML, except it utilizes a more sophisticated system to solve just about anything that requires the human “thought.” DL consists of artificial neural networks, which are brain-inspired systems that replicate how humans think and learn. While ML algorithms are generally linear, DL uses a hierarchical method to analyze data and produce more accurate results.
Deep Learning is the key technology behind self-driving cars, online recommendation feature on Netflix, fraud detection, and a host of other developments including our very own QARAsoft’s Market Dreamer. What makes Deep Learning great is that it has the ability to adjust according to its environment. Whereas ML requires an engineer to intervene a few times, DL has the power to determine its own errors. Deep Learning is essentially the closest thing to a human brain. It focuses more narrowly on a subset of ML tools and techniques and mimics our very own decision-making process.
So there you have it. A simple and (hopefully) easy guide to help you understand Artificial Intelligence better.
